Aims and objectives To explore whether the risk of peripheral venous catheters failure remained constant throughout catheter use in adult patients. Background Peripheral venous catheters, widely used in adult patients, may have a critical threshold dwell time associated with increased risk of catheter failure. Design Prospective, observational study. We have complied with the STROBE checklist of items. Methods This study was conducted from July–October 2018 in Hunan, China. Data on patient factors, catheter factors and catheter failure events were collected. Poisson regression was used to assess the effect of catheter dwell time on catheter failure while adjusting for other variables. Results A total of 1,477 patients were included in the analysis. There were 854 cases (57.8%) of catheter failure. The median dwell time to catheter failure was 52 hr (interquartile range: 36–73 hr). The incidence rate of catheter failure significantly increased by 1.1%/h in the first 38 hr after catheter insertion. From 39–149 hr, the incidence rate significantly decreased, and at >149 hr, there was no significant change in the incidence rate. Meanwhile, factors such as vascular quality and infused drugs showed having an impact on catheter failure events. Conclusions The risk of catheter failure may not remain constant throughout the dwell time. The results suggest that nurses should assess the insertion site frequently in the first 38 hr. Relevance to clinical practice The significant increase in the risk of catheter failure per hour may warrant close and frequent inspection of insertion site during the first 38 hr.

Purpose: Limited risk assessment tool to stratify the risk of PICC-related thrombosis (PICC-RVT) in breast cancer patients. This study developed a model to assess the risk of PICC-RVT in breast cancer patients.Methods: We conducted a retrospective cohort study of 1284 breast cancer patients receiving PICC insertion during 1 January 2015 -31 August 2019 at a cancer specialized hospital in Hunan province, China. The entire population is divided into two groups at a ratio of 3:1 which included a derivation sample (n=978), and a validation sample (n=284). PICC-RVT was con rmed by ultrasonography in the presence of clinical symptoms and signs.Results: PICC-RVT occurred in 40 (4.09%) of the derivation sample patients. Multivariable analysis identi ed 9 variables: chronic obstructive pulmonary disease, prior central venous catheter placement, higher level of Platelets, higher level of D-dimer, lower level of Activated partial thromboplastin time, menopause, no prior breast surgery, upper extremity lymphedema, and endocrine therapy. Points were assigned to each variable according to regression coe cient. The model had an area under the receiver operating characteristics curve (AUC) of 0.850 (95% CI 0.776 to 0.924), The Hosmer-Lemeshow goodness of t was 4.781 (p=0.572). At a cutoff value of 3.5, the sensitivity and specifcity were 75% and 83%, respectively.Conclusion: Several disease-speci c factors of breast cancer (e.g., menopause, endocrine therapy and upper extremity lymphedema) play important roles in the development of PICC-RVT. Patients at higher PICC-RVT risk could be candidates for close post-insertion monitoring and interventions to prevent PICC-RVT.

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Purpose: Limited risk assessment tool to stratify the risk of PICC-related thrombosis (PICC-RVT) in breast cancer patients. This study developed a model to assess the risk of PICC-RVT in breast cancer patients. Methods: We conducted a retrospective cohort study of 1284 breast cancer patients receiving PICC insertion during 1 January 2015 - 31 August 2019 at a cancer specialized hospital in Hunan province, China. The entire population is divided into two groups at a ratio of 3:1 which included a derivation sample (n=978), and a validation sample (n=284). PICC-RVT was confirmed by ultrasonography in the presence of clinical symptoms and signs. Results: PICC-RVT occurred in 40 (4.09%) of the derivation sample patients. Multivariable analysis identified 9 variables: chronic obstructive pulmonary disease, prior central venous catheter placement, higher level of Platelets, higher level of D-dimer, lower level of Activated partial thromboplastin time, menopause, no prior breast surgery, upper extremity lymphedema, and endocrine therapy. Points were assigned to each variable according to regression coefficient. The model had an area under the receiver operating characteristics curve (AUC) of 0.850 (95% CI 0.776 to 0.924), The Hosmer-Lemeshow goodness of fit was 4.781 (p=0.572). At a cutoff value of 3.5, the sensitivity and specifcity were 75% and 83%, respectively.Conclusion: Several disease-specific factors of breast cancer (e.g., menopause, endocrine therapy and upper extremity lymphedema) play important roles in the development of PICC-RVT. Patients at higher PICC-RVT risk could be candidates for close post-insertion monitoring and interventions to prevent PICC-RVT.

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Background: Hospitalized cancer patients suffer a high risk of venous thromboembolism (VTE). Guidelines suggested performing a personalized thromboprophylaxis guided by VTE risk assessment tools. Machine learning (ML) has advantages in data processing and model development. The study aimed to develop predictive models using four different ML methods and to compare their predictive performance.Methods: A retrospective case-control study was conducted from October 1, 2021 to February 30, 2022 in Hunan Cancer Hospital. A total of 1,100 hospitalized cancer patients were included. The outcome variable was the occurrence of VTE during hospitalization. Input variables, including patient, tumor, treatment and laboratory indicators characteristics, were trained for four ML models: Logistic Regression, Support Vector Machine, Random Forest, and extreme gradient boosting (XGBoost). Model performance was compared using the area under the receiver operating characteristic curve (AUROC). Features rankings were achieved according to the permutation scores of selected features in the optimal model.Results: A total of 1,100 patients (mean [SD] age, 54.75[11.08] years; 485[44.09%] male) were included in the study. There were 340 patients in the VTE group and 760 patients in the non-VTE group. XGBoost model showed the best predictive performance among four models, with AUROC value of 0.818 (95%CI: 0.762, 0.870). Performance of other three models were lower with the following AUROCs in the testing set: Logistic Regression, 0.757（95%CI: 0.689,0.816）; Support Vector Machine, 0.759（95%CI: 0.697,0.818）; and Random Forest, 0.743（95%CI: 0.678,0.808）. The most five significant features in XGBoost model were D-dimer, diabetes, hypertension, pleural metastasis and hematological malignancies. Conclusion: Four predictive models were developed using ML algorithms. XGBoost model was the optimal predictive model compared to other three ML models (Logistic Regression, Support Vector Machine, and Random Forest). This study indicates that ML may play an important role in VTE risk estimation among hospitalized cancer patients and provide reference for thromboprophylaxis.